Overcoming the curse of dimensionality in dynamic programming by tensor decompositions

Dolgov, Sergey (PI)

Department of Mathematical Sciences

Project: Research council

4 Article
1 Conference article

4 results

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2023
DATA-DRIVEN TENSOR TRAIN GRADIENT CROSS APPROXIMATION FOR HAMILTON-JACOBI-BELLMAN EQUATIONS
Dolgov, S., Kalise, D. & Saluzzi, L., 31 Oct 2023, In: SIAM Journal on Scientific Computing. 45, 5, p. A2153-A2184
Research output: Contribution to journal › Article › peer-review

Open Access
1 Citation (SciVal)
TTRISK: Tensor train decomposition algorithm for risk averse optimization
Antil, H., Dolgov, S. & Onwunta, A., 31 May 2023, In: Numerical Linear Algebra with Applications. 30, 3, e2481.
Research output: Contribution to journal › Article › peer-review

Open Access
6 Citations (SciVal)
2022
A quantum-inspired approach to exploit turbulence structures
Gourianov, N., Lubasch, M., Dolgov, S., van den Berg, Q. Y., Babaee, H., Givi, P., Kiffner, M. & Jaksch, D., 31 Jan 2022, In: Nature Computational Science . 2, 1, p. 30-37 8 p.
Research output: Contribution to journal › Article › peer-review

Open Access
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43 Citations (SciVal)

120 Downloads (Pure)
2021
Tensor decomposition methods for high-dimensional Hamilton-Jacobi-Bellman equations
Dolgov, S., Kalise, D. & Kunisch, K. K., 31 May 2021, In: SIAM Journal on Scientific Computing. 43, 3, p. A1625-A1650 26 p.
Research output: Contribution to journal › Article › peer-review

Open Access
43 Citations (SciVal)